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SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction

Official code for the ISBI 2024 paper: SimSAM: Zero-shot Medical Image Segmentation via Simulated Interaction, which was awarded as a top 12 oral paper.

Requirements

• datasets
• monai
• numpy
• opencv-python
• tifffile
• torch
• transformers

The relevant packages can be installed with:

pip install -r requirements.txt

This repo also uses surface-distance, which should be downloaded and included in the root folder.

Downloading the datasets

This paper makes use of three datasets, which need to be separately downloaded.

• Breast Ultrasound Scans can be downloaded from Kaggle.
• CVC ClinicDB is available here.
• ISIC 2016 is available here (you should download Task 1). Downloaded content should be placed in the data/ folder.

Evaluation

You can evaluate either the baseline or the simsam model with the eval.py script, by selecting from one of the following datasets: ["busi", "cvc", "isic"]. For example:

python scripts/eval.py --model_load_path facebook/sam-vit-base \
    --dataset cvc \
    --model_type simsam \

Training

You can also fine-tune the SAM model on any of the datasets with:

python scripts/train.py --model_load_path facebook/sam-vit-base \
    --model_save_path path/to/save/model \
    --dataset cvc \
    --learning_rate 1e-5 \
    --num_train_epochs 10 \